Zobrazeno 1 - 10
of 295
pro vyhledávání: '"Leonard, John J"'
Semantic Simultaneous Localization and Mapping (SLAM) systems struggle to map semantically similar objects in close proximity, especially in cluttered indoor environments. We introduce Semantic Enhancement for Object SLAM (SEO-SLAM), a novel SLAM sys
Externí odkaz:
http://arxiv.org/abs/2411.06752
This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can
Externí odkaz:
http://arxiv.org/abs/2410.00117
We introduce SeaSplat, a method to enable real-time rendering of underwater scenes leveraging recent advances in 3D radiance fields. Underwater scenes are challenging visual environments, as rendering through a medium such as water introduces both ra
Externí odkaz:
http://arxiv.org/abs/2409.17345
Autor:
Singh, Kurran, Leonard, John J.
Underwater object-level mapping requires incorporating visual foundation models to handle the uncommon and often previously unseen object classes encountered in marine scenarios. In this work, a metric of semantic uncertainty for open-set object dete
Externí odkaz:
http://arxiv.org/abs/2409.11555
Autor:
Zhang, Yihao, Leonard, John J.
Category-level object pose and shape estimation from a single depth image has recently drawn research attention due to its wide applications in robotics and self-driving. The task is particularly challenging because the three unknowns, object pose, o
Externí odkaz:
http://arxiv.org/abs/2408.13147
Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the ability to segment and identify nearly all objects in the world. Howeve
Externí odkaz:
http://arxiv.org/abs/2404.04377
Despite recent advances in semantic Simultaneous Localization and Mapping (SLAM) for terrestrial and aerial applications, underwater semantic SLAM remains an open and largely unaddressed research problem due to the unique sensing modalities and the o
Externí odkaz:
http://arxiv.org/abs/2403.12837
Autor:
Huang, Qiangqiang, Leonard, John J.
Inferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior i
Externí odkaz:
http://arxiv.org/abs/2303.14283
We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial ob
Externí odkaz:
http://arxiv.org/abs/2303.07308
We study landmark-based SLAM with unknown data association: our robot navigates in a completely unknown environment and has to simultaneously reason over its own trajectory, the positions of an unknown number of landmarks in the environment, and pote
Externí odkaz:
http://arxiv.org/abs/2302.13264